Invasive wildlife often causes serious damage to the economy and agriculture as well as environmental, human and animal health. Habitat models can fill knowledge gaps about species distributions and assist planning to mitigate impacts. Yet, model accuracy and utility may be compromised by small study areas and limited integration of species ecology or temporal variability. Here we modelled seasonal habitat suitability for wild pigs, a widespread and harmful invader, in northern Australia. We developed a resource-based, spatially-explicit and regional-scale approach using Bayesian networks and spatial pattern suitability analysis. We integrated important ecological factors such as variability in environmental conditions, breeding requirements and home range movements. The habitat model was parameterized during a structured, iterative expert elicitation process and applied to a wet season and a dry season scenario. Model performance and uncertainty was evaluated against independent distributional data sets. Validation results showed that an expert-averaged model accurately predicted empirical wild pig presences in northern Australia for both seasonal scenarios. Model uncertainty was largely associated with different expert assumptions about wild pigs’ resource-seeking home range movements. Habitat suitability varied considerably between seasons, retracting to resource-abundant rainforest, wetland and agricultural refuge areas during the dry season and expanding widely into surrounding grassland floodplains, savanna woodlands and coastal shrubs during the wet season. Overall, our model suggested that suitable wild pig habitat is less widely available in northern Australia than previously thought. Mapped results may be used to quantify impacts, assess risks, justify management investments and target control activities. Our methods are applicable to other wide-ranging species, especially in data-poor situations.
Invasive alien plant species threaten agriculture and biodiversity globally and require ongoing management to minimise impacts. However, the large number of invasive species means that a risk-based approach to prioritisation is needed, taking into account the spatial scale of management decisions and myriad of available information. Here, we developed a risk-based inventory of invasive plants in Queensland, Australia, using both current species distribution/abundance and the severity of their impacts. Our assessment followed a comprehensive data collection process including a scoping of local government pest management plans, herbarium records, the published literature and structured elicitation of expert knowledge during a series of regional stakeholder workshops. From~300 plant species that were identified as established and/or emerging invaders in the State, only one-third were considered by practitioners to pose significant risks across regions to be considered management priorities. We aggregated regional species lists into a statewide priority list and analysed the data set (107 species) for historical, geographical, floristic and ecological patterns. Regions on the mainland eastern seaboard of the State share similar invasive plant communities, suggesting that these regions may form a single management unit, unlike the western/inland and the extreme far north (Torres Strait Islands) regions, which share fewer invasive plant species. Positive correlations were detected between invasiveness and time since introduction for some but not all plant life forms. Stakeholders identified research and management priorities for the invasive plant list, including biological control options, public awareness/education, effective herbicide use, ecology/taxonomy and risk analysis. In the course of the exercise, a statewide invasive plant priority list of high-, medium-and low-impact scores for policy, research and management was compiled. Finally, our approach to invasive plant species prioritisation highlighted that planning and policy documents are not necessarily reflected at the grassroot level in terms of species identity and management priorities.We subjected the regional priority lists to a quantitative risk assessment, with the aim of generating a priority list to inform State government policy and research investments
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